Lateral spatial biases in naturalistic and simulated driving: Does pseudoneglect influence performance?
Bibliographic record
Abstract
Whereas a rightward bump is more likely than a leftward bump when walking through a doorway, investigations into potential similar asymmetries for drivers are limited. The research presented here aims to determine the influence of innate lateral spatial biases when driving. Data from the Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and a driving simulation were used to address our research questions. Data points from SHRP 2 were aggregated within relevant variables (e.g., left/right obstacles). In the simulation, participants drove in ways that were consistent with their everyday driving in urban and rural environments. Collision frequency, collision severity and average lateral lane position were analyzed with rightward biases throughout both analyzes. SHRP 2 data indicated greater likelihoods of collisions when vehicles crossed the right line/edge of the road and when making a right turn. There were more collisions with obstacles on the right side, which were also more severe, and greater rightward lane deviations in the driving simulation, contrasted with more severe collisions on the left side in SHRP 2 data, possibly because of the presence of traffic. These findings suggest that previously observed rightward biases in distant space when walking are also present when driving.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".